Because real-world clinical settings are neither perfectly IID over time nor replete with adversarial shifts, we consider a problem setting in between these two extremes.
In particular, we suppose the distributional shifts are bounded in terms of the maximum mean discrepancy [1]}, though they may still be adaptive within the prescribed bounds.
We investigate a learning-to-approve (L2A) approach that learns how to approve algorithmic updates using labeled data sampled IID from the target population at each time point.
L2A defines a broad family of approval strategies, which encompasses strategies designed for IID data as well as adversarial setups.
It searches over this family by dynamically weighting candidate strategies according to the exponentially weighted average forecaster (EWAF), a classical solution from the prediction-with-expert-advice literature [2]}.
